A typical task assignment system algorithmically assigns tasks arriving at a task assignment center to agents available to handle those tasks. At times, the task assignment center may be in an “L1 state” and have agents available and waiting for assignment to tasks. At other times, the task assignment center may be in an “L2 state” and have tasks waiting in one or more queues for an agent to become available for assignment.
In some typical task assignment centers, tasks are assigned to agents ordered based on time of arrival, and agents receive tasks ordered based on the time when those agents became available. This strategy may be referred to as a “first-in, first-out,” “FIFO,” or “round-robin” strategy. For example, in an L2 environment, when an agent becomes available, the task at the head of the queue would be selected for assignment to the agent.
In other typical task assignment centers, a performance-based routing (PBR) strategy for prioritizing higher-performing agents for task assignment may be implemented. Under PBR, for example, the highest-performing agent among available agents receives the next available task. Other PBR and PBR-like strategies may make assignments using specific information about the agents.
“Behavioral Pairing” or “BP” strategies, for assigning tasks to agents, improve upon traditional assignment methods. BP targets balanced utilization of agents while simultaneously improving overall task assignment center performance potentially beyond what FIFO or PBR methods will achieve in practice.
In some task assignment systems, it may be advantageous to consider the next-best action for a task given its assignment to a particular agent. Thus, it may be understood that there may be a need for a decisioning BP strategy that takes into consideration the next-best action for a task-agent pairing in order to optimize the overall performance of the task assignment system.